Prepopulation of call center cache

ABSTRACT

A system, process, and computer-readable medium for updating an application cache using a stream listening service is described. A stream listening service may monitor one or more data streams for content relating to a user. The stream listening service may forward the content along with time-to-live values to an application cache. A user may use an application to obtain information regarding the user&#39;s account, where the application obtains information from a data store and/or cached information from the application cache. The stream listening service, by forwarding current account information, obtained from listening to one or more streams, to the application cache, reduces traffic at the data store by providing current information from the data stream to the application cache.

STATEMENT OF RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.17/533,618, entitled, “Stream Listening Cache Updater,” filed Nov. 23,2021. The present application is also a continuation of U.S. applicationSer. No. 17/533,773, entitled “Prepopulation of Caches,” filed Nov. 23,2021. The contents of these applications are hereby expresslyincorporated by reference, in their entireties, for all purposes.

FIELD OF USE

Aspects of the disclosure relate generally to exchanging informationbetween networked devices.

BACKGROUND

Service providers store data for later retrieval by consumers. Often thedata is received via one or more data streams describing events relatedto consumers (e.g., medical records from visits, network authenticationattempts, and/or transactions with merchants). To balance consumers'requests for their data, service providers segment how consumers' datais stored to lessen the processing burden on any one storage system andalso to reduce the response time to the consumer because in-memory cacheis much faster to access than retrieving the data from the data store(also referred to as persistent storage). For example, in addition tosending data, from a single data store, to consumers based on theirrequests for their data, some service providers separately cache thedata in a separate cache. In the event the consumers request their dataa short time later, data stored in the cache may be sent first and, ifthe desired data is not in the cache, the data may be obtained fromother data stores of the service provider. To minimize stale data fromresiding in the cache, service providers assign limited time-to-live(TTL) values to the data stored in cache. Often, TTLs are stored aseither a counter or a timestamp. At the expiration of the TTL, e.g., theevent count or timespan reached, the data is discarded. With longerTTLs, there is a greater chance that data is stale. Serving stale datato consumers creates consumer dissatisfaction with the serviceproviders' ability to provide timely data to the consumers. With shorterTTLs, the service providers' other storage systems bear more of theburden providing data to consumers. Due to the spiky nature of consumerdemand for data, the service providers using shorter TTLs need to expandthe capabilities of their other storage systems to be ready for thespikes in consumer demand for data.

SUMMARY

The following presents a simplified summary of various aspects describedherein. This summary is not an extensive overview, and is not intendedto identify key or critical elements or to delineate the scope of theclaims. The following summary merely presents some concepts in asimplified form as an introductory prelude to the more detaileddescription provided below.

Aspects described herein may address these and other problems, andgenerally improve how data may be provided to consumers. In additionalaspects, based on the improvements in the ability to provide timely datato consumers, service providers may be able to reduce overengineeringtheir storage systems while maintaining the ability to timely deliverinformation to consumers, thus providing better consumer experiencesusing their services. The improved services may be at least based onmodifying how received data is stored in an application cache. In one ormore aspects, the cache may be refreshed based on data from the serviceprovider's data store and also refreshed based on data from one or moredata streams. Other aspects may comprise caching information from thedata streams in the application cache for all application users and/ormay limit the caching of information to only those application userswith information currently in the application cache. Further aspects maycomprise improving how applications, provided by a service provider foraccessing the consumers' data, interact with the cache and the serviceprovider's data stores. By efficiently storing current data in a cache,timely data may be provided to consumers with decreasing a processingburden on the service provider's data stores and, thus, improve overallexperiences for the service provider's consumers.

According to some aspects, a computer-implemented method may comprisereceiving, by a server and from an application, a request for firstinformation associated with a user and storing the first information inan application cache, wherein the application cache assigns atime-to-live value to the first information. The method may furthercomprise monitoring a data stream of incoming information, detectingsecond information in the data stream associated with the user, whereinthe second information comprises a change to the first information. Themethod may further comprise storing, based on the detecting the secondinformation, the second information in the application cache, whereinthe second information overwrites the first information and refreshesthe time-to-live value, receiving, from the application, a request forthe second information associated with the user, and sending, to theapplication, the second information.

According to other aspects, these and other benefits may be achieved byusing a computer-implemented method that may comprise receiving, at adata store, a data stream comprising information in fields; receiving,at a server, the data stream; determining, at the server and from thefields in the data stream, one or more time-to-live (TTL) valuesassociated with the fields; and storing, in an application cache, theinformation and the one or more TTL values associated with theinformation, wherein the one or more TTL values are based on one or morefields of the information. The computer-implemented method may alsocomprise deleting, based on an expiration of a first TTL valueassociated with first information, the first information from theapplication cache; receiving, from an application, a request for thefirst information of a first field, related to a user, and secondinformation of a second field, related to the user; determining, at theapplication cache, that the second information of the second field, ofthe user, is currently stored in the application cache; and sending,based on a determination that the second information in the second fieldof the user is currently stored in the application cache, the secondinformation to the application.

In one or more examples, the method may comprise receiving, from anapplication, user interaction information; predicting, using amachine-learning model trained on previous interactions of previoususers with their applications and with call centers and based on thereceived user interaction information, information expected to be neededby a call center when responding to an inquiry from the user; andpopulating a call center cache based on a prediction for the user. Themethod may also comprise storing, in an application cache and with atime-to-live value, first information for the application; monitoring adata stream of incoming information; detecting second information in thedata stream; and storing, based on the detecting the second information,the second information in the application cache, wherein the secondinformation overwrites the first information and refreshes thetime-to-live value. In one or more examples, the method may furthercomprise receiving interactions between the call center and the user;and retraining, based on the interactions between the call center andthe user and based on the previous interactions of the previous userswith their applications and with the call centers, the machine-learningmodel.

In one or more examples, the method may comprise receiving, from anapplication, user interaction information; predicting, using amachine-learning model trained on previous interactions of previoususers with their applications and with call centers and based on thereceived user interaction information, a likelihood of a user of theapplication to contact a call center with an inquiry from the user; andpopulating a call center cache based on a prediction for the user tocontact a call center. The method may further comprise authenticatingthe user based on the prediction for the user; storing, in anapplication cache and with a time-to-live value, first information forthe application; monitoring a data stream of incoming information;detecting second information in the data stream; and storing, based onthe detecting the second information, the second information in theapplication cache, wherein the second information overwrites the firstinformation and refreshes the time-to-live value. In one or moreexamples, the method may further comprise receiving interactions betweenthe call center and the user; and retraining, based on the interactionsbetween the call center and the user and based on the previousinteractions of the previous users with their applications and with thecall centers, the machine-learning model.

A computer-implemented process may include receiving, by a server andfrom an application, first information from an application cacheassociated with the application. The process may include prepopulating afirst action cache with at least some of the first information receivedfrom the application cache, receiving a first request, by the server andfrom the application, a first request comprising a request forperforming a first action, and performing the first action. The processmay include prepopulating, with second information and based on theperformance of the first action, a second action cache with at leastsome of the first information from the first action cache, receiving asecond request, by the server and from the application, a second requestcomprising a request for performing a second action, determining, by theserver and based on the second information in the prepopulated secondcache, whether to perform the second action, and based on adetermination to perform the second action, performing the secondaction.

In additional examples, the method may additionally include storing thefirst information in the first action cache, wherein the first actionassigns a time-to-live value to the first information and deleting, atan expiration of the time-to-live value of the first information, thefirst information from the first action cache. The method may includedetermining, based on receiving the first request from the application,whether to perform the first action, wherein performing the first actionis based on a determination to perform the first action. The method mayinclude receiving, by the server and from the application, a thirdrequest comprising a request for performing the first action,determining, by the server and based on the first information in theprepopulated first action cache, whether to perform the third request'sfirst action, and based on a determination to not perform the thirdrequest's first action, denying the third request.

The first action may include opening a new account, and the first actioncache may include a cache of information from which a determination ofwhether the first request for the first action is fraudulent. The secondaction may include performing a new transaction, and the second actioncache may include a cache of information from which a determination ofwhether the second request for the second action is fraudulent.

The method may further include monitoring a data stream of incominginformation, detecting user-specific information in the data streamassociated with a user associated with the application, wherein theuser-specific information may include a change to applicationinformation in an application cache associated with the application,storing, based on the detecting the user-specific information, theuser-specific information in the application cache, wherein theuser-specific information overwrites existing user-specific informationand refreshes a time-to-live value associated with the user-specificinformation in the application cache, receiving, from the application, arequest for the user-specific information associated with the user, andsending, to the application, the user-specific information.

The method may further include detecting second user-specificinformation in the data stream associated with the user, wherein thesecond user-specific information may include information not currentlystored in the application cache, and storing, based on the detecting thesecond user-specific information, the second user-specific informationin the application cache, wherein the second user-specific informationreceives a second time-to-live value, wherein the receiving the requestfor the user-specific information may include receiving a request forthe user-specific information and the second user-specific information,and wherein sending the user-specific information may include sending,to the application, the user-specific information and the seconduser-specific information.

The method may include receiving, from the application, a request forthird user-specific information associated with the user, determiningthe application cache does not currently store the third user-specificinformation, receiving, from the server, the third user-specificinformation and a third time-to-live value, storing, in the applicationcache, the third user-specific information and the third time-to-livevalue, and sending, to the application, the third user-specificinformation.

A system of one or more computers may be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs may be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions. Assuch, corresponding apparatus, systems, and computer-readable media arealso within the scope of the disclosure.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIG. 1 depicts an example of a computing device and system architecturethat may be used in implementing one or more aspects of the disclosurein accordance with one or more illustrative aspects discussed herein;

FIG. 2 depicts a block diagram of an environment in which systems and/ormethods described herein may be implemented;

FIG. 3 depicts a block diagram showing various process flows forupdating an application cache;

FIG. 4 shows various examples of how data changes based on whether astream listening cache service is used;

FIGS. 5-7 depict examples of processes for updating application cachesusing data from data streams;

FIG. 8 depicts an example content in a data stream and the assignment ofTTLs;

FIG. 9 depicts an example of using a machine-learning model to predictissues of users to be addressed by a call center;

FIG. 10 depicts an example of using a machine-learning model to predictwhen users are likely to contact a call center; and

FIG. 11 depicts a block diagram showing various process flows for usinginteractions associated with a first cache to prepopulate a secondcache.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration various embodiments in whichaspects of the disclosure may be practiced. It is to be understood thatother embodiments may be utilized and structural and functionalmodifications may be made without departing from the scope of thepresent disclosure. Aspects of the disclosure are capable of otherembodiments and of being practiced or being carried out in various ways.Also, it is to be understood that the phraseology and terminology usedherein are for the purpose of description and should not be regarded aslimiting. Rather, the phrases and terms used herein are to be giventheir broadest interpretation and meaning. The use of “including” and“comprising” and variations thereof is meant to encompass the itemslisted thereafter and equivalents thereof as well as additional itemsand equivalents thereof. Any sequence of computer-implementableinstructions described in this disclosure may be considered to be an“algorithm” as those instructions are intended to solve one or moreclasses of problems or to perform one or more computations. Whilevarious directional arrows are shown in the figures of this disclosure,the directional arrows are not intended to be limiting to the extentthat bi-directional communications are excluded. Rather, the directionalarrows are to show a general flow of steps and not the unidirectionalmovement of information. In the entire specification, when an element isreferred to as “comprising” or “including” another element, the elementshould not be understood as excluding other elements so long as there isno special conflicting description, and the element may include at leastone other element. In addition, the terms “unit” and “module”, forexample, may refer to a component that exerts at least one function oroperation, and may be realized in hardware or software, or may berealized by combination of hardware and software. In addition, termssuch as “ . . . unit”, “ . . . module” described in the specificationmean a unit for performing at least one function or operation, which maybe implemented as hardware or software, or as a combination of hardwareand software. Throughout the specification, expression “at least one ofa, b, and c” may include ‘a only’, ‘b only’, ‘c only’, ‘a and b’, ‘a andc’, ‘b and c’, and/or ‘all of a, b, and c’.

By way of introduction, aspects discussed herein may relate to methodsand techniques for storing incoming data from one or more data streamsin an application cache while also storing the data in one or more datastores. One or more aspects may generally relate to improving howconstantly changing data is provided to a user. Content of a data storechanges over time. In some examples, one or more stream listeningservices may subscribe to the stream of changes from the data store. Inother examples, stream data may also flow from a source to the datastore, where the stream being received by the data store may also bereceived by the stream listening service. In general, the stream dataincludes different types of information (transaction IDs, card numbers,account numbers, user names, merchant names, etc.). Additionally oralternatively, the information stored in the application cache may beused to prepopulate other caches. Additionally or alternatively, auser's interactions with their account may be used to prepopulate acache related to monitoring different account interactions.

When a user accesses an application to obtain information regarding theuser's information (e.g., event history including medical history,financial transactions, shopping history, and/or any status and/orhistory relating to the user), the application queries an applicationcache for the user's information. If the information is in theapplication cache, the application provides the information to the user.If the information is not in the application cache, the applicationqueries a data store for the information. At least some of theinformation received from the data store is provided to the user and atleast some of the information is stored in the application cache. TTLvalues may be assigned to the information stored in the applicationcache. Upon expiration of the TTL values, the associated informationstored in the application cache may be deleted. For future requests fromthe user's application for information, the application again queriesthe application cache. If the information still resides in theapplication cache, the information may be provided to the user. If theinformation is not in the application cache (e.g., never previously inthe application cache or previously in the application cache but deletedas its associated TTL had expired and the information deleted), theapplication queries the data store for the information.

One or more aspects described herein include updating the informationstored in the application cache from the incoming data stream and/ordata streams as opposed to solely updating the application cache fromthe data store. For instance, the application cache may be subscribed toa service configured to listen to data streams for information relevantto the individual users. When the listening service receives informationrelevant to a user, the service may determine a TTL value or values forthe information and forward the information and the TTL value or valuesto the application cache.

In one example, a user may check the user's account balance using theapplication. The application may determine that the current accountbalance is not currently stored in the application cache and request thecurrent account balance from the data store. The application cache mayreceive a current account balance from the data store and provide thecurrent account balance to the user. The stream listening service maydetect a transaction related to the user (e.g., via matching a useridentification, account identification, or the like). The streamlistening service may forward information relating to the transaction tothe application cache, e.g., a transaction amount. The application cachemay determine an updated account balance by subtracting the transactionamount from the account balance stored in the application cache. Theapplication cache may update the stored account balance with the newlycalculated account balance. Also, the TTL value may be refreshed toextend its lifetime.

Additionally or alternatively, the user's interactions with theapplication cache may be used by a first machine learning model topredict one or more issues for handling by a call center and prepopulatea cache of the call center with the user's information. Additionally oralternatively, the user's interactions with the application cache may beused by a second machine learning model to predict if, and when, theuser is likely to contact a call center and, based on that prediction,modify an authentication procedure used by the call center toauthenticate the user. Additionally or alternatively, the user'sinteractions with the user's account may be used by a third machinelearning model to predict possible future user actions and prepopulateanother cache based on those predictions.

Some of the advantages described herein include improving the timelinessof information stored in the application cache. Other advantages includereducing the quantity of queries sent to the data store to obtain users'information. Further advantages may include prepopulating other cachesbased on a users' interactions with the application and/or theiraccount.

As described herein, the application may also be augmented usingartificial intelligence. The artificial intelligence may comprise one ormore machine learning models. The one or more machine learning modelsmay comprise a neural network, such as a convolutional neural network(CNN), a recurrent neural network, a recursive neural network, a longshort-term memory (LSTM), a gated recurrent unit (GRU), an unsupervisedpre-trained network, a space invariant artificial neural network, agenerative adversarial network (GAN), or a consistent adversarialnetwork (CAN), such as a cyclic generative adversarial network (C-GAN),a deep convolutional GAN (DC-GAN), GAN interpolation (GAN-INT), GAN-CLS,a cyclic-CAN (e.g., C-CAN), or any equivalent thereof. The neuralnetwork may be trained using supervised learning, unsupervised learning,back propagation, transfer learning, stochastic gradient descent,learning rate decay, dropout, max pooling, batch normalization, longshort-term memory, skip-gram, or any equivalent deep learning technique.Additionally or alternatively, the machine learning model may compriseone or more decisions trees, a support vector machine, logisticregression, random forest, or equivalents thereof.

In one or more aspects, the artificial intelligence may be trained toreview and/or analyze (e.g., scrape) the records stored in one or moreexisting caches of one or more users and, to the extent relevant, if,and when, those users contacted call centers. The artificialintelligence may be used to identify the parties and/or processes,authenticate relationships across accounts, digitally verify data fromshared sources, validate compliance with laws and regulations, and/oridentify potential fraud. The one or more machine learning models may betrained using supervised learning, unsupervised learning, backpropagation, transfer learning, stochastic gradient descent, learningrate decay, dropout, max pooling, batch normalization, long short-termmemory, skip-gram, or any equivalent deep learning technique. Once theone or more machine learning models are trained, the one or more machinelearning models may be exported and/or deployed to prepopulate one ormore caches, predict when events are likely to occur, and/or assist inthe authentication of account-related events. For instance, thepredictions from a machine-learning model may be used to analyze and/orreview records associated with account-related events to reduce the riskassociated with that account and/or other accounts.

In some embodiments, the machine learning models may be existing machinelearning models. In further embodiments, the machine learning models maybe proprietary models. Alternatively, the machine learning models may bemodified existing machine learning models such that the machine learningmodels become proprietary. In some instances, the machine learningmodels may be trained using different parameters, such as backpropagation, transfer learning, stochastic gradient descent, learningrate decay, dropout, max pooling, batch normalization, long short-termmemory, skip-gram, and/or any equivalent deep learning technique. Usingthis information, the machine-learning models may be trained or evenfurther trained to refine the machine learning models.

Before discussing these concepts in greater detail, however, severalexamples of a computing device that may be used in implementing and/orotherwise providing various aspects of the disclosure will first bediscussed with respect to FIG. 1 . FIG. 1 illustrates one example of acomputing device 101 that may be used to implement one or moreillustrative aspects discussed herein. For example, the computing device101 may, in some embodiments, implement one or more aspects of thedisclosure by reading and/or executing instructions and performing oneor more actions based on the instructions. In some embodiments, thecomputing device 101 may represent, be incorporated in, and/or includevarious devices such as a desktop computer, a computer server, a mobiledevice (e.g., a laptop computer, a tablet computer, a smart phone, anyother types of mobile computing devices, and the like), and/or any othertype of data processing device.

The computing device 101 may, in some embodiments, operate in astandalone environment. In others, the computing device 101 may operatein a networked environment. As shown in FIG. 1 , various network nodes101, 105, 107, and 109 may be interconnected via a network 103, such asthe Internet. Other networks may also or alternatively be used,including private intranets, corporate networks, LANs, wirelessnetworks, personal networks (PAN), and the like. Network 103 is forillustration purposes and may be replaced with fewer or additionalcomputer networks. A local area network (LAN) may have one or more ofany known LAN topologies and may use one or more of a variety ofdifferent protocols, such as Ethernet. Devices 101, 105, 107, 109, andother devices (not shown) may be connected to one or more of thenetworks via twisted pair wires, coaxial cable, fiber optics, radiowaves, or other communication media. Additionally or alternatively, thecomputing device 101 and/or the network nodes 105, 107, and 109 may be aserver hosting one or more databases.

As seen in FIG. 1 , the computing device 101 may include a processor111, RAM 113, ROM 115, network interface 117, input/output interfaces119 (e.g., keyboard, mouse, display, printer, etc.), and memory 121.Processor 111 may include one or more computer processing units (CPUs),graphical processing units (GPUs), and/or other processing units such asa processor adapted to perform computations associated with databaseoperations. Input/output 119 may include a variety of interface unitsand drives for reading, writing, displaying, and/or printing data orfiles. Input/output 119 may be coupled with a display such as display120. Memory 121 may store software for configuring computing device 101into a special purpose computing device in order to perform one or moreof the various functions discussed herein. Memory 121 may storeoperating system software 123 for controlling overall operation of thecomputing device 101, control logic 125 for instructing the computingdevice 101 to perform aspects discussed herein, database creation andmanipulation software 127 and other applications 129. Control logic 125may be incorporated in and may be a part of database creation andmanipulation software 127. In other embodiments, the computing device101 may include two or more of any and/or all of these components (e.g.,two or more processors, two or more memories, etc.) and/or othercomponents and/or subsystems not illustrated here.

Devices 105, 107, 109 may have similar or different architecture asdescribed with respect to the computing device 101. Those of skill inthe art will appreciate that the functionality of the computing device101 (or device 105, 107, 109) as described herein may be spread acrossmultiple data processing devices, for example, to distribute processingload across multiple computers, to segregate transactions based ongeographic location, user access level, quality of service (QoS), etc.For example, devices 101, 105, 107, 109, and others may operate inconcert to provide parallel computing features in support of theoperation of control logic 125 and/or software 127.

One or more aspects discussed herein may be embodied in computer-usableor readable data and/or computer-executable instructions, such as in oneor more program modules, executed by one or more computers or otherdevices as described herein. Generally, program modules includeroutines, programs, objects, components, data structures, etc. thatperform particular tasks or implement particular abstract data typeswhen executed by a processor in a computer or other device. The modulesmay be written in a source code programming language that issubsequently compiled for execution, or may be written in a scriptinglanguage such as (but not limited to) Python or JavaScript. The computerexecutable instructions may be stored on a computer readable medium suchas a hard disk, optical disk, removable storage media, solid-statememory, RAM, etc. As will be appreciated by one of skill in the art, thefunctionality of the program modules may be combined or distributed asdesired in various embodiments. In addition, the functionality may beembodied in whole or in part in firmware or hardware equivalents such asintegrated circuits, field programmable gate arrays (FPGA), and thelike. Particular data structures may be used to more effectivelyimplement one or more aspects discussed herein, and such data structuresare contemplated within the scope of computer executable instructionsand computer-usable data described herein. Various aspects discussedherein may be embodied as a method, a computing device, a dataprocessing system, or a computer program product. Having discussedseveral examples of computing devices which may be used to implementsome aspects as discussed further below, discussion will now turn to amethod for listening to data streams.

FIG. 2 is a block diagram of an environment in which systems and/ormethods described herein may be implemented. As shown in FIG. 2 , theenvironment may include servers 201 and 202 and a computing device 203connected by a network 204. The devices, servers, and network may beinterconnected via wired connections, wireless connections, or acombination of wired and wireless connections. The server 201 may bedirected toward receiving files relating to activities from computingdevice 203 and then sending the files to server 202 for processing.

The network 204 may include one or more wired and/or wireless networks.For example, network 204 may include a cellular network (e.g., along-term evolution (LTE) network, a code division multiple access(CDMA) network, a 3G network, a 4G network, a 5G network, another typeof next generation network, etc.), a public land mobile network (PLMN),a local area network (LAN), a wide area network (WAN), a metropolitanarea network (MAN), a telephone network (e.g., the Public SwitchedTelephone Network (PSTN)), a private network, an ad hoc network, anintranet, the Internet, a fiber optic-based network, a cloud computingnetwork, or the like, and/or a combination of these or other types ofnetworks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2 . Furthermore, two or more servers shown in FIG. 2 maybe implemented within a single server, or a single server shown in FIG.2 may be implemented as multiple, distributed servers or in acloud-based computing environment. Additionally, or alternatively, a setof devices (e.g., one or more devices) of the environment 203 mayperform one or more functions described as being performed by anotherset of devices of the environment. Network 204 may be represented as asingle network but may comprise combinations of other networks orsubnetworks. In one or more examples, a data stream (not shown) may bereceived by server 201, where server 201 is a data store for receivedinformation. The data stream may also be received by server 202. Server202 may also store a cache for a user application (e.g., referred toherein as an “application cache”). The server 202 may parse the incomingdata stream and, where relevant for users using applications that relyon the server 202's application cache, the server 202 may extractinformation from the data stream, determine one or more TTL values to bestored with the information, and store the extracted information and theTTL values for access by the users' application. The application mayrequest information first from the application cache in server 202. Ifthe desired and/or current information is not in the application cache,a query for the information may be sent to server 201, the data storefor information received for the incoming data stream. The informationfrom server 201 may be both sent to the computing device 203 and storedin the application cache in 202. TTLs may be generated by either theserver 201, the sever 202, or a combination of each of servers 201 and202.

A process of listening to a data stream and updating an applicationcache is described herein. For purposes of explanation, the process isdescribed in the following sections: Updating Application Cache from aData Stream; Processes for Updating Application Caches from DataStreams; Processes for Prepopulating a Call Center Cache; and Processesfor Prepopulating a Cache based on Previous Account Interactions.

Updating Application Cache from a Data Stream

FIG. 3 depicts a block diagram showing various process flows forupdating an application cache. FIG. 3 comprises a data store 301configured to store users' information. As users interact with otherentities, those interactions are received and stored in the data store301. An event data stream 302 may be subscribed to by a stream listeningcache service 307. The event data stream 302 may comprise events, suchas, visiting a doctor and a user's medical record being updated,visiting a financial institution and securing a loan, making a purchaseat a merchant, or the like. The type and quantity of events is notlimited. The event data stream 302 is shown generally as path A and pathB. The event data stream 302 may comprise single events pertaining to asingle user and/or multiple events of multiple users and/or anycombination thereof. For instance, the event data stream 302 maycomprise information in one or more fields where the informationidentifies an account identification, a transaction type, a transactionamount or change amount, and/or other information that describes the oneor more events.

FIG. 3 also shows a client device 304 (e.g., device 101) receivinguser's input (e.g., via path D). The client device 304 may include anapplication 305 executing in one or more processors of the client device304. For instance, the user may request the application 305 to obtainthe user's current account balance based on a quantity of recenttransactions (e.g., events) made by the user. The application 305 firstattempts to obtain information (e.g., via path E) from an applicationcache 306. The application cache 306 may reside inside one or moreservers and/or someplace else (e.g., in a cloud-accessible storage).Information available in the application cache 306 is sent to theapplication via path F. For information not available in the applicationcache 306, the application 305 requests (via path H) the missinginformation from the data store 301. Additionally or alternatively, theapplication cache 306 may request the missing information from the datastore 301. Information from the data store 301 may be sent to theapplication 305 via path I and the information (from either theapplication cache 306, the data store 301, or a combination of theapplication cache 306 and the data store 301).

As described herein, one or more aspects relate to separately listeningto the event data stream 302 and providing parsed stream data to theapplication cache 306. As shown in FIG. 3 , a stream listening cacheservice 307 (e.g., provided by a separate server—for instance, server202 of FIG. 2 —or a server used for another purpose as described herein)listens to the event data stream 302 and provides new data, via path C,to the application cache 306. The stream listening cache service 307 mayprocess all streamed data or may process only a subset of the streameddata. In an example where only a subset of the streamed data from theevent stream 302 is processed, the stream listening cache service 307may receive (e.g., via path K) user information 308 from the applicationcache 306 regarding information currently stored in the applicationcache 306. The dataset describing the user information 308 may be acomplete copy of the data in the current application cache, may be adataset that identifies, for each user, which fields of informationand/or types of information are currently stored in the applicationcache (e.g., account balance, most recent transaction, loan information,account information, etc.), and/or may be a dataset that identifies theusers that have current data in the application cache 306. Using theuser information 308 (e.g., identifying the actual values of data in thecache for specific users, identifying the fields of data in the cachefor the specific users, and/or identifying the specific users that haveat least some data in the application cache), the stream listening cacheservice 307 may listen specifically for data in the event data stream302 that relates to the user information 308. If the stream listeningcache service 307 determines that event stream data 302 relates to theuser information 308, the stream listening cache service 307 may extractthe information from the event data stream 302, determine one or moreTTL values for the extracted information, and send (via path C) theextracted information and the one or more TTL values for storage in theapplication cache 306. Based on the extracted information, theapplication cache 306 may replace existing values with new values fromthe extracted information from the stream listening cache service 307,may additionally store the new values from the extracted informationfrom the stream listening cache service 307 while retaining existingvalues in the application cache 306 for the user, and/or may calculatereplacement values based on adjusting existing values using the newvalues from the extracted information from the stream listening cacheservice 307. For example, to calculate a replacement value for anexisting account balance (the existing account balance stored in theapplication cache 306), the application cache 306 may receive (as a newvalue from the extracted information from the stream listening cacheservice 307) a transaction amount. The application cache 306 maysubtract the transaction amount from the existing account balance,resulting in a new account balance. In this example, the actual accountbalance was not received as part of the information from the data streambut was calculated based on the transaction amount from the data streamand the existing account balance stored in the application cache 306.Additionally or alternatively, the calculation of the replacement valuesmay occur in the stream listening cache service 307 (e.g., where theactual values from the application cache 306 are received with the userinformation 308 and the stream listening cache service 307 subtracts thedetected transaction amount from the event data stream 302 from thereceived account balance from the application cache 306).

Additionally or alternatively, the stream listening cache service 307may not receive user information 308 and rather process all informationfor all users and provide that information to the application cache 306.Additionally or alternatively, the stream listening cache service 307may have a separate list of users of the application 305 and, based onevent stream data 302 relating to users on that separate list of users,may extract the relevant information from the event data stream 302 andforward it to the application cache 306.

The information received by the application cache 306 from the streamlistening service 307 may be assigned a single TTL value (e.g.,assigning the same TTL value for each of the user's name, accountnumber, transaction identification, merchant, transaction amount,account balance, etc.) such that, at the expiration of the TTL value,all information in the application cache associated with that TTL valueis deleted. In a further example, the information received may beassigned different TTL values (e.g., assigning different TTL values foreach of the user's name, account number, transaction amount, and/oraccount balance, etc.). Because the user's name and/or account numberchanges slowly (if at all), that information may be assigned a longerTTL (e.g., 5 minutes, 5 days, 5 months, etc.) as desired. Because otherinformation (e.g., the transaction amount and/or account balance)changes more often, that information becomes stale more quickly and thatinformation may be assigned a shorter TTL (e.g., 30 seconds, 1 minute,10 minutes).

In one or more examples, the TTLs may be assigned fixed TTLs based onthe type of information. For example, the information may be delimitedas content in separate fields and the TTLs assigned to the content basedon the fields. For instance, first content in an “account number” fieldmay be assigned a first TTL and second content an “account balance”field may be assigned a second TTL, where the first TTL is differentfrom the second TTL. Additionally or alternatively, the TTLs may beassigned to the information based on the origin of the information. Forinstance, information received from a first data stream may be assignedone TTL and information received from a second data stream may beassigned another TTL, where the two TTLs are different. In yet anotherexample, information received from the data store may be assigned oneTTL and information received from a data stream may be assigned anotherTTL, where the data store-assigned TTL and the data stream listenerservice-assigned TTL are different.

The stream listening cache service 307 may determine one or more TTLvalues for the records. One TTL value may be assigned for each parsedrecord. Additionally or alternatively, one or more fields of a parsedrecord may receive a first TTL value and other fields of the parsedrecord may receive other TTL values that are different in length orduration from the first TTL value. Additionally or alternatively, theapplication cache 306 may determine and/or update TTL values forinformation stored in the application cache 306. For instance, where areplacement account balance is determined in the application cache 306,the application cache 306 may generate a new TTL value for thereplacement account balance and/or update an existing TTL value toextend its life.

FIG. 4 provides four examples of how data is provided to application 305based on whether application cache 306 includes a value associated witha user and whether the stream listening cache service 307 exists. In theexamples of FIG. 4 , an event 302 occurs that changes modifies dataassociated with a user from a first value to a second value. Forexample, a user purchases a product from a merchant and the user'saccount balance changes based on the transaction. For reference, inexamples 401-404, the rows corresponding to the events are identified as“Event/Event Result” as the data that may be available in the event datastream 302 of FIG. 3 may be an actual value (e.g., a transaction IDnumber, merchant name, a transaction amount, etc.) from the event, maybe a value that, when combined with other information, is a resultingvalue (e.g., a remaining balance—calculated from subtracting atransaction amount from a customer's existing balance, a remainingamount left on a mortgage and/or lien, etc.), and/or a combination ofactual values and calculated values. Example 401 describes interactionsbetween a data store 301, an application cache 306, and an application305 in which no stream listening cache service 307 is used and noinitial value is present in the application cache 306. Example 402describes interactions between the data store 301, the application cache306, and the application 305 in which a stream listening cache service307 is used and no initial value is present in the application cache306. Example 403 describes interactions between the data store 301, theapplication cache 306, and the application 305 in which no streamlistening cache service 307 is used and an initial value is present inthe application cache 306. Example 404 describes interactions betweenthe data store 301, the application cache 306, and the application 305in which a stream listening cache service 307 is used and an initialvalue is present in the application cache 306.

In example 401, an initial value of XYZ (e.g., an initial accountbalance of the user) is stored in the data store 301. Because the userhas not recently requested the value from data store in a while, anycached value in the application cache 306 has already been deleted—e.g.,because of the expiration of an associate TTL value. In example 401, thestream listening cache service 307 is not used. An event 302 occurs thataffects that initial value XYZ (e.g., the user purchases a producthaving a cost that, when subtracted from value XYZ, results in valueQR). The event data stream includes a change that, when processed by thedata store 301, results in a new value QR. A user controls theapplication 305 on the client device 304 to obtain information regardingthe user's account. The application 305 first attempts (via path E) toobtain the information from the application cache 306. Because none ofthe user's information remains in the application cache 306 (identifiedto the application 305 via path F), the application 305 requests theinformation, via path H, from the data store 301 and receives it viapath I. The application 305 provides the information to the user viapath G. Because the application cache 306 did not have any informationof the user, the application 305 obtained the most current information(value QR) from the data store 301 and provided it to the user.

In example 402, the stream listening cache service 307 exists, e.g.,stream listening cache service 307, of FIG. 3 , configured to receivethe event data stream 302, via path B, and provide information and TTLvalues, via path C, to the application cache 306. In example 402, theinitial value of XYZ (e.g., an initial account balance of the user) isstored in the data store 301. Because the user has not recentlyrequested the value from data store in a while, any cached value in theapplication cache 306 has already been deleted—e.g., because of theexpiration of an associate TTL value. An event 302 occurs that affectsthat initial value XYZ (e.g., the user purchases a product having a costthat, when subtracted from value XYZ, results in value QR). The eventdata stream includes a change that, when processed by the data store301, results in a new value QR. Because the stream listening cacheservice exists in example 402, the stream listening cache serviceresponds to the event 302 by forwarding the event/event result QR, alongwith one or more TTL values, to the application cache 306. In theexample 402, the value stored in application cache is QR. Additionallyor alternatively, information may be forwarded to the application cachethat prompts the application cache to query the data store 301 foradditional information to determine the resulting value QR.

Next, a user controls the application 305 on the client device 304 toobtain information regarding the user's account. The application 305first attempts (via path E) to obtain the information from theapplication cache 306. Because the stream listening cache serviceforwarded information to the application cache 306 that resulted invalue QR being stored (directly or via additional interactions with thedata store 301), the application cache 306 has the value QR and is ableto provide it to the application 305. Compared to example 401, theexample 402 with the stream listening cache service provides the benefitof reducing the quantity of queries sent from the application cache 306to the data store 301 for information.

In example 403, no stream listening cache service exists. An initialvalue LMN is stored in both the data store 301 and in the applicationcache 306. For instance, a user may have recently queried theapplication 305 for information. In response, the application 305obtained value LMN from the data store 301 and provided it to both theuser and the application cache 306 (along with one or more TTL values).A new event is received with the event/event result value XY and isstored in the data store 301. Next, a user controls the application 305on the client device 304 to obtain information regarding the user'saccount. The application 305 first attempts (via path E) to obtain theinformation from the application cache 306. Because the applicationcache 306 already has value LMN and the TTL value or values have notexpired, the application cache 306 provides the value LMN to theapplication 305. However, the actual event/event value is XY and is onlystored in the data store 301. Eventually, once the TTL value or valuesexpire and the user subsequently operates the application, theapplication queries the data store 301 for the value and provides it tothe user. In other words, the user may be initially provided incorrectinformation but will eventually receive the correct information. Whilethe time delay between receiving the incorrect information and thecorrect information may be small (e.g. 5 minutes, an hour, etc.), usersmay become frustrated based on the lack of the application 305 toconsistently provide correct data.

Further, based on examples 401 and 403, in situations where the variousvalues in the application cache 305 have TTL values of differentlengths, users may be provided with odd combinations of fresh and staledata. For instance, if a user changes a value during a call withcustomer service and that value is associated with a long TTL value(e.g., a corrected address) and the application cache 306 still has theold (now stale) value in it, any new event containing values not alreadyin the application cache 306 will appear as fresh (and accurate) values(based on example 401) while the information changed during the callwill be stale (based on example 403) because of the information existingin the application cache 306 while fresher information exists in thedata store 301.

In example 404, the stream listening cache service 307 exists. Aninitial value LMN is stored in both the data store 301 and in theapplication cache 306. For instance, a user may have recently queriedthe application 305 for information. In response, the application 305obtained value LMN from the data store 301 and provided it to both theuser and the application cache 306 (along with one or more TTL values).A new event is received with the event/event result value XY and thevalue XY is stored in the data store 301. Because the stream listeningcache service exists in example 404, the stream listening cache serviceresponds to the event 302 by forwarding the event/event result XY, alongwith one or more TTL values, to the application cache 306. In theexample 404, the value stored in application cache is XY. Additionallyor alternatively, information may be forwarded to the application cachethat prompts the application cache to query the data store 301 foradditional information to determine the resulting value QR. Next, a usercontrols the application 305 on the client device 304 to obtaininformation regarding the user's account. The application 305 firstattempts (via path E) to obtain the information from the applicationcache 306. Because the application cache 306 already has value XY andthe TTL value or values, associated with the value XY have not expired,the application cache 306 provides the value XY to the application 305.

Processes for Updating Application Caches from Data Streams

FIGS. 5-7 depict examples of processes for updating application cachesusing data from data streams. In FIG. 5 , a stream listening cacheservice (concatenated in the figure as “stream cache”) receives userinformation in step 500 and an event data stream in step 501. In someexamples, the stream listening cache service may not receive userinformation 500 but instead have a predefined list of users and/or mayprocess an event stream for all users. It is appreciated that step 500may be included or not included as desired. In step 502, the streamlistening cache service parses information from the data stream. Theparsing of step 502 may be based on the combination of the userinformation of step 500 and the event data stream of step 501 or may bebased on the event data stream 501 and possibly including otherinformation. In step 503, the stream listening cache service assigns TTLvalues based on the types of values in the parsed information. In step504, the stream listening cache service sends, to an application cache,information and associated TTL values. In step 505, the applicationcache stores the information and the TTL values. In step 506, anapplication receives a request for information from a user. In step 507,the application determines whether the information currently exists inthe application cache. If the information exists in the applicationcache, then, in step 508, the information is sent to the user, forinstance, via an application controlling a display to display thereceived information.

If the information does not currently exist in the application cache,the application and/or the application cache requests, in step 509, theinformation from the data store. In step 510, the information isreceived from the data store. In some instances, the information may bereceived with one or more TTL values (as data 511). In other instances,the application and/or the application cache may determine (in step 512)one or more TTLs to store with the received information. In step 513,the information and the one or more TTLs are stored in the applicationcache. At the expiration of a TTL, the information associated with thatTTL is deleted from the application cache. Additionally oralternatively, the application and/or the data store may monitor auser's activities in step 517 and, based on those activities, predict instep 518 that the user may be interested in obtaining information usingthe application. Based on that determination, the application mayrequest updated information from the data store to prepare for thepossible user request for information. Additionally or alternatively,the data store may proactively forward information to the applicationand/or to the application cache for satisfying the user's request forinformation.

In FIG. 6 , a stream listening cache service receives user informationin step 600 and an event data stream in step 601. In some examples, thestream listening cache service may not receive user information 600 butinstead have a predefined list of users and/or may process an eventstream for all users. It is appreciated that step 600 may be included ornot included as desired. In step 602, the stream listening cache serviceparses information from the data stream, for instance, to extractinformation relating to one or more specific users. The parsing of step602 may be based on the combination of the user information of step 600and the event data stream of step 601 or may be based on the event datastream 601 and possibly including other information. In step 603, thestream listening cache service may assign, based on a type or types ofinformation received, one or more TTL values to the information. In step604, the stream listening cache service sends, to an application cache,information and the associated TTLs. In step 605, the application cachestores the information and the assigned TTLs. In step 606, anapplication receives a user request for information. The applicationdetermines, in step 607, whether the information exists in theapplication cache. If the information exists in the application cache,then, in step 608, the information is sent to the user, for instance,via an application controlling a display to display the receivedinformation.

If the information does not currently exist in the application cache,the application and/or the application cache requests, in step 609, theinformation from the data store. In step 610, the information isreceived from the data store and forwarded to the user in step 608. Insome instances, the information may be received with one or more TTLvalues (as data 611). In other instances, the application and/or theapplication cache may determine (in step 612) one or more TTLs to storewith the received information. In step 613, the information and the oneor more TTLs are stored in the application cache. At the expiration of aTTL, the information associated with that TTL is deleted from theapplication cache. Additionally or alternatively, from step 602, thestream listening cache service may filter the information parsed fromthe data stream based on types of information and only assign, in step603, TTLs to information relating to selected types. Additionally oralternatively, from step 602, the stream listening cache service maydetermine, in step 615, whether a user-specific filter exists forvarious data types. For instance, a user may desire that identificationinformation relating to the user and merchants be stored in theapplication cache but not want financial amounts be stored in theapplication cache. If the stream listening cache service determines, instep 615, that no user-specific filter has been set, then the streamlistening cache service assigns, in step 603, TTL values to informationrelating to the information. If the stream listening cache servicedetermines, in step 615, that a user-specific filter has been set, thenthe stream listening cache service filters, in step 616, the informationby the desired types and then assigns, in step 603, TTL values to thefiltered information.

FIG. 7 depicts a process of responding to a user request for informationwhere the information stored in the application cache may or may not bestale. For instance, the TTL value assigned to the information in theapplication cache may be about to expire (within a few seconds, within afew minutes, and the like). Because the value stored in the applicationcache may be different from a current value stored in the data store,the application attempts to update the value from the data store. Instep 700, an application receives a request for information. The requestmay be similar to the request received in step 606 in FIG. 6 . In step701, the application determines whether the information exists in theapplication cache. If the information does not currently exist in theapplication cache, then the application interacts with the data store asshown in steps 609-613 as described above with respect to FIG. 6 . Ifthe information does exist, the application cache may attempt to obtainone or more incremental changes that have occurred since the value waslast updated. For instance, changes to values may originate outside ofthe data stream or data streams. For reference, the application cachemay be operating in a delta-type environment 702 in which it attempts toobtain one or more delta changes (changes since a previous point) andapply those changes to the value in the application cache. In step 703,the application and/or application cache requests the delta changes fromthe data store. In step 704, the delta changes are received from thedata store. In step 705, the value in the application cache is updatedbased on the delta changes from the data store. In step 706, the updatedvalue is provided to the user. The TTL value associated with the updatedvalue may be updated as well and/or the TTL value may be permitted toexpire and force the application to obtain fresh data from the datastore the next time the user operates the application to obtain theuser's data.

FIG. 8 depicts an example content in a data stream and the assignment ofTTLs. The data stream is represented as a streaming table 801 withrecords {Record A, Record B, and Record C} with data arranged in fields{user identification, user name, updated account balance, transactionamount, transaction declined, behavior change, and transactionidentification}. Additional and/or fewer records and/or fields may beused as desired. Table 802 comprises the fields and TTL values for thefields. Some TTL values may be the same as some TTL values and differentfor other TTL values. The data stream 801 may be parsed, TTL values fromtable 802 assigned, and combined in table 803 data to be sent to anapplication cache for a user (e.g., the user related to Record A of thethree records of data stream 801).

Based on the above, a computer-implemented method may comprisereceiving, by a server and from an application, a request for firstinformation associated with a user and storing the first information inan application cache, wherein the application cache assigns atime-to-live value to the first information. The method may furthercomprise monitoring a data stream of incoming information, detectingsecond information in the data stream associated with the user, whereinthe second information comprises a change to the first information. Themethod may further comprise storing, based on the detecting the secondinformation, the second information in the application cache, whereinthe second information overwrites the first information and refreshesthe time-to-live value, receiving, from the application, a request forthe second information associated with the user, and sending, to theapplication, the second information.

In additional aspects, the computer-implemented method may furthercomprise detecting third information in the data stream associated withthe user, wherein the third information comprises information notcurrently stored in the application cache, and storing, based on thedetecting the third information, the third information in theapplication cache, wherein the third information receives a secondtime-to-live value. The request may include a request for the secondinformation and the third information. Both the second information andthe third information may be sent to the user. In one or more aspects,the method may further comprise receiving, from the application, arequest for third information associated with the user, determining theapplication cache does not currently store the third information,receiving, from the server, the third information and a secondtime-to-live value, storing, in the application cache, the thirdinformation and the second time-to-live value, and sending, to theapplication, the third information.

Additionally or alternatively, a computer-implemented method maycomprise receiving, at a data store, a data stream comprisinginformation in fields; receiving, at a server, the data stream;determining, at the server and from the fields in the data stream, oneor more time-to-live (TTL) values associated with the fields; andstoring, in an application cache, the information and the one or moreTTL values associated with the information, wherein the one or more TTLvalues are based on one or more fields of the information. Thecomputer-implemented method may also comprise deleting, based on anexpiration of a first TTL value associated with first information, thefirst information from the application cache; receiving, from anapplication, a request for the first information of a first field,related to a user, and second information of a second field, related tothe user; determining, at the application cache, that the secondinformation of the second field, of the user, is currently stored in theapplication cache; and sending, based on a determination that the secondinformation in the second field of the user is currently stored in theapplication cache, the second information to the application.

In one or more additional aspects, the method may further comprisedetermining, at the server and from the data stream, a third fieldcomprises third information; determining, at the server, a fourth fieldis currently stored; updating, based on the third information, thefourth field; and storing an updated TTL value associated the fourthfield. The third information may be associated with a change in anaccount balance of the user, the fourth field may be an account balance,and updating the current account balance of the fourth field may resultin an updated account balance.

In further aspects, the method may comprise determining, at theapplication cache, that the first information, of the user, is notcurrently stored in the application cache; sending, to the data storeand based on a determination that the first information of the user isnot currently stored in the application cache, a request for the firstinformation in the first field associated with the user; receiving, fromthe data store, the first information associated with the first field;determining, based on the first field, a second TTL value for the firstinformation received from the data store; storing, in the applicationcache, the first information in the first field and the second TTLvalue; and sending the first information to the application.

In some examples, the first TTL value and the second TTL value may bethe same while, in other examples, they may be different. The lifespanof a TTL value may be determined by determining, from a table and forthe first field, the first TTL value associated with the first field;and determining, from the table and for the second field, a second TTLvalue associated with the second field. In further aspects, a thirdfield in the data stream may be determined and, based on a determinationof the third field in the data stream, the process may send, to the datastore, a request for fourth information, associated with the user, in afourth field. The method may further comprise receiving, from the datastore, the fourth information, associated with the user, in the fourthfield; determining, based on the fourth field, a second TTL value forthe fourth information received from the data store; storing, in theapplication cache, the fourth information and the second TTL value;receiving, from the application, a request for the fourth information;and sending, to the application, the fourth information. In one or moreexamples, the third field may indicate a transaction associated with theuser has been declined, a change in behavior of the user, or a change inan account balance of an account associated with the user. The fourthfield may comprise a most recent transaction associated with an accountof the user.

In some aspects, the method may further comprise determining, at theserver and based on the fields in the data stream, updated secondinformation is being received; and determining, from the data stream,that the updated second information has been received in its entirety.The sending the second information to the application may be delayeduntil after a determination that the entirety of the updated secondinformation has been received by the application cache.

In some aspects, the method may further comprise receiving, at theapplication cache and for the user, a modification of the one or moreTTL values to be associated with the fields from the data stream. Thedetermining the one or more TTL values may comprise determining, basedon the modification of the one or more TTL values for the user, one ormore modified TTL values, for the information of the user, associatedwith the fields, and the storing the information and the one or more TTLvalues may comprise storing, in the application cache, the information,of the user, and the one or more modified TTL values.

An apparatus, in accordance with various aspects, may comprise one ormore processors; and memory storing instructions that, when executed bythe one or more processors, cause the apparatus to receive a datastream, wherein the data stream comprises information in fields;determine, from the fields in the data stream, time-to-live (TTL) valuesassociated with the fields; store, in an application cache, theinformation and TTL values associated with the fields of theinformation; and delete, from the application cache, with a first TTLvalue associated with first information, and the first TTL value havingexpired, the first information from the application cache. Theinstructions may further cause the apparatus to receive, from anapplication, a request, wherein the request is for the first informationof a first field, related to a user, and for second information of asecond field, related to the user; determine that the first information,of the user, is not currently stored in the application cache and thatthe second information, of the user, is currently stored in theapplication cache; send, to a data store and based on a determinationthat the first information of the user is not currently stored in theapplication cache, a request for the first information associated withthe user; receive, from the data store, the first information associatedwith the user; and send, to the application, based on a determinationthat the second information of the user is currently stored in theapplication cache and based on the received first information, the firstinformation and the second information.

In one or more aspects, the instructions may further cause the apparatusto determine, based on the first field, a second TTL value for the firstinformation received from the data store and store, in the applicationcache, the first information in the first field and the second TTLvalue. The first and second TTL values may be the same or different. Theinstructions may further cause the apparatus to determine, from the datastream, a third field comprising third information; determine a fourthfield is currently stored; update, based on the third information, thecurrent account balance in the fourth field to an updated accountbalance; and store an updated TTL value associated with the updatedaccount balance in the fourth field.

In one or more further aspects, the instructions may cause the apparatusto determine a third field in the data stream; based on a determinationof the third field in the data stream, send a request for fourthinformation, associated with the user, in a fourth field; receive thefourth information, associated with the user, in the fourth field;determine, based on the fourth field, a second TTL value for thereceived fourth information; store the fourth information and the secondTTL value; receive, from the application, a request for the fourthinformation; and send, to the application, the fourth information. Insome examples, the fourth field may comprise a most recent transactionassociated with an account of the user. In further examples, the thirdfield may indicate a transaction associated with the user has beendeclined, a change in behavior of the user, or a change in an accountbalance of an account associated with the user. In additional examples,the third information may be associated with a change in an accountbalance of the user, the fourth field may comprise a current accountbalance, and the instructions may cause the apparatus to update, basedon the third information, the current account balance of the fourthfield, resulting in an updated account balance.

A non-transitory media storing instructions that, when executed by oneor more processors, cause the one or more processors to perform stepscomprising receiving, at a data store, a data stream comprisinginformation in fields; receiving, at a server, the data stream;determining, from a table, a first TTL value associated with a firstfield of the fields and a second TTL value associated with a secondfield of the fields, wherein the first TTL value and the second TTLvalue are different; storing, in the application cache, firstinformation in the first field and the first TTL value and secondinformation in the second field and the second TTL value; and deleting,from the application cache and upon expiration of the first TTL valueassociated with the first stored information, the first storedinformation. The steps may further comprise receiving, from anapplication, a request for the first information, related to a user, andfor the second information of the second field, related to the user;determining, at the application cache, that the second information ofthe second field, of the user, is currently stored in the applicationcache; and sending, based on a determination that the second informationin the second field of the user is currently stored in the applicationcache, the second information to the application.

Processes for Prepopulating a Call Center Cache

FIG. 9 depicts an example of using a machine-learning model to predictissues of users to be addressed by a call center. Steps 501-506 and 508of FIG. 9 correspond to steps 501-506 and 508, respectively, of FIG. 5 .In step 900, the application cache retrieves any needed information fromthe data store. For example, the retrieval may comprise any of theapproaches described herein including, but not limited to the approachesof FIGS. 5-7 . In step 901, the application sends information regardinghow the user has interacted with the application to a provider of theuser's account (e.g., a bank, financial institution, merchant, and thelike). In step 902, a trained machine-learning model is provided withthe user's interactions with the application. Based on the user'sinteractions with the application and having been trained on previousinteractions between users, their applications, and subsequent contactsmade by those users with the topics addressed by the call center, themachine learning model predicts the subject matter and/or theinformation needed by the call center to respond to the user's inquiry.Based on that prediction in step 902, a call center cache may bepopulated (via step 903) with information relating to the predictionsfrom step 902. In step 904, if, and when, the specific user contacts thecall center, the individual or individuals responding to the specificuser's inquiry address the specific user in step 904. The results and/orresolution of the specific user's inquiry may be forwarded from step 904to step 905 where the machine learning model is further trained toimprove its ability to predict if, and when, a user will contact thecall center. The resulting newly trained machine learning model may beused to predict future information needed for call centers in step 902based on future interactions of users with their applications.

FIG. 10 depicts an example of using a machine-learning model to predictwhen users are likely to contact a call center. Steps 501-506 and 508 ofFIG. 10 correspond to steps 501-506 and 508, respectively, of FIG. 5 .In step 1000, the application cache retrieves any needed informationfrom the data store. For example, the retrieval may comprise any of theapproaches described herein including, but not limited to the approachesof FIGS. 5-7 . In step 1001, the application sends information regardinghow the user has interacted with the application to a provider of theuser's account (e.g., a bank, financial institution, merchant, and thelike). In step 1002, a trained machine-learning model is provided withthe user's interactions with the application. Based on the user'sinteractions with the application and having been trained on previousinteractions between users, their applications, and subsequent contactsmade by those users with the topics contacting the call center (e.g.,within a given time window—for instance, within an hour, a day, a week,within a billing cycle, or the like), the machine learning modelpredicts when the user is expected to call and, possibly, the subjectmatter of the user's inquiry. Based on that prediction in step 1002, acall center cache may be populated (via step 1003) with informationrelating to the predictions from step 1002 including the likelihood auser will contact the call center based on the user's interactions withthe application.

When a user contacts the call center, the degree of authenticationrequired by the user may be modified based on whether the user waspredicted to contact the call center in step 1002. Next, in step 1004,the system receives an inquiry from the user. The inquiry may include achat request, a telephone call, a video call, email, other instantmessage, and/or contact via other medium or even in person at a physicallocation of the provider of the user's account. If the user's inquirywas predicted (determined in step 1005), the user may be authenticatedin step 1006 using a lower authentication threshold (e.g., verifying oneor two security items—e.g., last four digits of a social securitynumber, home zip code, etc.). The results of the authentication in step1006 and/or the user's inquiry may be provided to the machine-learningmodel and the machine learning model retrained in step 1007 using thenew information. If the user's inquiry was not predicted (from step1005), the user may be authenticated in step 1008 using a higherauthentication threshold (e.g., verifying three or more security items).The number of security items per threshold is variable and may beincreased or decreased as desired. The results of the authentication instep 1008 and/or the user's inquiry may be provided to themachine-learning model and the machine learning model retrained in step1007 using the new information.

Based on the above, a computer-implemented method may comprisereceiving, from an application, user interaction information;predicting, using a machine-learning model trained on previousinteractions of previous users with their applications and with callcenters and based on the received user interaction information,information expected to be needed by a call center when responding to aninquiry from the user; and populating a call center cache based on aprediction for the user. The method may also comprise storing, in anapplication cache and with a time-to-live value, first information forthe application; monitoring a data stream of incoming information;detecting second information in the data stream; and storing, based onthe detecting the second information, the second information in theapplication cache, wherein the second information overwrites the firstinformation and refreshes the time-to-live value.

In one or more examples, the method may further comprise receivinginteractions between the call center and the user; and retraining, basedon the interactions between the call center and the user and based onthe previous interactions of the previous users with their applicationsand with the call centers, the machine-learning model.

In additional aspects, the computer-implemented method may furthercomprise detecting third information in the data stream associated withthe user, wherein the third information comprises information notcurrently stored in the application cache, and storing, based on thedetecting the third information, the third information in theapplication cache, wherein the third information receives a secondtime-to-live value. The request may include a request for the secondinformation and the third information. Both the second information andthe third information may be sent to the user. In one or more aspects,the method may further comprise receiving, from the application, arequest for third information associated with the user, determining theapplication cache does not currently store the third information,receiving, from the server, the third information and a secondtime-to-live value, storing, in the application cache, the thirdinformation and the second time-to-live value, and sending, to theapplication, the third information.

Additionally or alternatively, a computer-implemented method maycomprise receiving, from an application, user interaction information;predicting, using a machine-learning model trained on previousinteractions of previous users with their applications and with callcenters and based on the received user interaction information,information expected to be needed by a call center when responding to aninquiry from the user; and populating a call center cache based on aprediction for the user. The method may also comprise receiving, fromthe application, a request for first information associated with theuser; storing the first information in an application cache, wherein theapplication cache assigns a time-to-live value to the first information;monitoring a data stream of incoming information; detecting secondinformation in the data stream; and storing, based on the detecting thesecond information, the second information in the application cache,wherein the second information overwrites the first information andrefreshes the time-to-live value.

In one or more additional aspects, the method may further comprisereceiving interactions between the call center and the user; andretraining, based on the interactions between the call center and theuser and based on the previous interactions of the previous users withtheir applications and with the call centers, the machine-learningmodel.

In one or more additional aspects, the method may further comprisedetermining, at the server and from the data stream, a third fieldcomprises third information; determining, at the server, a fourth fieldis currently stored; updating, based on the third information, thefourth field; and storing an updated TTL value associated the fourthfield. The third information may be associated with a change in anaccount balance of the user, the fourth field may be an account balance,and updating the current account balance of the fourth field may resultin an updated account balance.

In further aspects, the method may comprise determining, at theapplication cache, that the first information, of the user, is notcurrently stored in the application cache; sending, to the data storeand based on a determination that the first information of the user isnot currently stored in the application cache, a request for the firstinformation in the first field associated with the user; receiving, fromthe data store, the first information associated with the first field;determining, based on the first field, a second TTL value for the firstinformation received from the data store; storing, in the applicationcache, the first information in the first field and the second TTLvalue; and sending the first information to the application.

In some examples, the first TTL value and the second TTL value may bethe same while, in other examples, they may be different. The lifespanof a TTL value may be determined by determining, from a table and forthe first field, the first TTL value associated with the first field;and determining, from the table and for the second field, a second TTLvalue associated with the second field. In further aspects, a thirdfield in the data stream may be determined and, based on a determinationof the third field in the data stream, the process may send, to the datastore, a request for fourth information, associated with the user, in afourth field. The method may further comprise receiving, from the datastore, the fourth information, associated with the user, in the fourthfield; determining, based on the fourth field, a second TTL value forthe fourth information received from the data store; storing, in theapplication cache, the fourth information and the second TTL value;receiving, from the application, a request for the fourth information;and sending, to the application, the fourth information. In one or moreexamples, the third field may indicate a transaction associated with theuser has been declined, a change in behavior of the user, or a change inan account balance of an account associated with the user. The fourthfield may comprise a most recent transaction associated with an accountof the user.

In some aspects, the method may further comprise determining, at theserver and based on the fields in the data stream, updated secondinformation is being received; and determining, from the data stream,that the updated second information has been received in its entirety.The sending the second information to the application may be delayeduntil after a determination that the entirety of the updated secondinformation has been received by the application cache.

In some aspects, the method may further comprise receiving, at theapplication cache and for the user, a modification of the one or moreTTL values to be associated with the fields from the data stream. Thedetermining the one or more TTL values may comprise determining, basedon the modification of the one or more TTL values for the user, one ormore modified TTL values, for the information of the user, associatedwith the fields, and the storing the information and the one or more TTLvalues may comprise storing, in the application cache, the information,of the user, and the one or more modified TTL values.

An apparatus, in accordance with various aspects, may comprise one ormore processors; and memory storing instructions that, when executed bythe one or more processors, cause the apparatus to receive, from anapplication, user interaction information; predict, using amachine-learning model trained on previous interactions of previoususers with their applications and with call centers and based on thereceived user interaction information, information expected to be neededby a call center when responding to an inquiry from the user; andpopulate a call center cache based on a prediction for the user. Theinstructions may further cause the apparatus to store, in an applicationcache and with a time-to-live value, first information for theapplication; monitor a data stream of incoming information; detectsecond information in the data stream; and store, based on the detectingthe second information, the second information in the application cache,wherein the second information overwrites the first information andrefreshes the time-to-live value.

In one or more examples, the apparatus may further comprise instructionsto determine, based on the first field, a second TTL value for the firstinformation received from the data store; and store, in the applicationcache, the first information in the first field and the second TTLvalue. In one or more aspects, the instructions may further cause theapparatus to determine, based on the first field, a second TTL value forthe first information received from the data store and store, in theapplication cache, the first information in the first field and thesecond TTL value. The first and second TTL values may be the same ordifferent. The instructions may further cause the apparatus todetermine, from the data stream, a third field comprising thirdinformation; determine a fourth field is currently stored; update, basedon the third information, the current account balance in the fourthfield to an updated account balance; and store an updated TTL valueassociated with the updated account balance in the fourth field.

In one or more further aspects, the instructions may cause the apparatusto determine a third field in the data stream; based on a determinationof the third field in the data stream, send a request for fourthinformation, associated with the user, in a fourth field; receive thefourth information, associated with the user, in the fourth field;determine, based on the fourth field, a second TTL value for thereceived fourth information; store the fourth information and the secondTTL value; receive, from the application, a request for the fourthinformation; and send, to the application, the fourth information. Insome examples, the fourth field may comprise a most recent transactionassociated with an account of the user. In further examples, the thirdfield may indicate a transaction associated with the user has beendeclined, a change in behavior of the user, or a change in an accountbalance of an account associated with the user. In additional examples,the third information may be associated with a change in an accountbalance of the user, the fourth field may comprise a current accountbalance, and the instructions may cause the apparatus to update, basedon the third information, the current account balance of the fourthfield, resulting in an updated account balance.

A non-transitory media storing instructions that, when executed by oneor more processors, cause the one or more processors to perform stepscomprising receiving, at a data store, a data stream comprisinginformation in fields; receiving, at a server, the data stream;determining, from a table, a first TTL value associated with a firstfield of the fields and a second TTL value associated with a secondfield of the fields, wherein the first TTL value and the second TTLvalue are different; storing, in the application cache, firstinformation in the first field and the first TTL value and secondinformation in the second field and the second TTL value; and deleting,from the application cache and upon expiration of the first TTL valueassociated with the first stored information, the first storedinformation. The steps may further comprise receiving, from anapplication, a request for the first information, related to a user, andfor the second information of the second field, related to the user;determining, at the application cache, that the second information ofthe second field, of the user, is currently stored in the applicationcache; and sending, based on a determination that the second informationin the second field of the user is currently stored in the applicationcache, the second information to the application.

Based on the above, a computer-implemented method may comprisereceiving, from an application, user interaction information;predicting, using a machine-learning model trained on previousinteractions of previous users with their applications and with callcenters and based on the received user interaction information, alikelihood of a user of the application to contact a call center with aninquiry from the user; and populating a call center cache based on aprediction for the user to contact a call center. The method may furthercomprise authenticating the user based on the prediction for the user;storing, in an application cache and with a time-to-live value, firstinformation for the application; monitoring a data stream of incominginformation; detecting second information in the data stream; andstoring, based on the detecting the second information, the secondinformation in the application cache, wherein the second informationoverwrites the first information and refreshes the time-to-live value.

In one or more examples, the method may further comprise receivinginteractions between the call center and the user; and retraining, basedon the interactions between the call center and the user and based onthe previous interactions of the previous users with their applicationsand with the call centers, the machine-learning model.

In additional aspects, the computer-implemented method may furthercomprise detecting third information in the data stream associated withthe user, wherein the third information comprises information notcurrently stored in the application cache, and storing, based on thedetecting the third information, the third information in theapplication cache, wherein the third information receives a secondtime-to-live value. The request may include a request for the secondinformation and the third information. Both the second information andthe third information may be sent to the user. In one or more aspects,the method may further comprise receiving, from the application, arequest for third information associated with the user, determining theapplication cache does not currently store the third information,receiving, from the server, the third information and a secondtime-to-live value, storing, in the application cache, the thirdinformation and the second time-to-live value, and sending, to theapplication, the third information.

Additionally or alternatively, a computer-implemented method maycomprise receiving, from an application, user interaction information;predicting, using a machine-learning model trained on previousinteractions of previous users with their applications and with callcenters and based on the received user interaction information, alikelihood of a user of the application to contact a call center with aninquiry from the user; and populating a call center cache based on aprediction for the user to contact a call center. The method may furthercomprise authenticating the user based on the prediction for the user;receiving, from the application, a request for first informationassociated with the user; storing, in the application cache and with atime-to-live value, first information for the application; monitoring adata stream of incoming information; detecting second information in thedata stream; and storing, based on the detecting the second information,the second information in the application cache, wherein the secondinformation overwrites the first information and refreshes thetime-to-live value.

In one or more examples, the method may further comprise receivinginteractions between the call center and the user; and retraining, basedon the interactions between the call center and the user and based onthe previous interactions of the previous users with their applicationsand with the call centers, the machine-learning model.

In one or more additional aspects, the method may further comprisedetermining, at the server and from the data stream, a third fieldcomprises third information; determining, at the server, a fourth fieldis currently stored; updating, based on the third information, thefourth field; and storing an updated TTL value associated the fourthfield. The third information may be associated with a change in anaccount balance of the user, the fourth field may be an account balance,and updating the current account balance of the fourth field may resultin an updated account balance.

In further aspects, the method may comprise determining, at theapplication cache, that the first information, of the user, is notcurrently stored in the application cache; sending, to the data storeand based on a determination that the first information of the user isnot currently stored in the application cache, a request for the firstinformation in the first field associated with the user; receiving, fromthe data store, the first information associated with the first field;determining, based on the first field, a second TTL value for the firstinformation received from the data store; storing, in the applicationcache, the first information in the first field and the second TTLvalue; and sending the first information to the application.

In some examples, the first TTL value and the second TTL value may bethe same while, in other examples, they may be different. The lifespanof a TTL value may be determined by determining, from a table and forthe first field, the first TTL value associated with the first field;and determining, from the table and for the second field, a second TTLvalue associated with the second field. In further aspects, a thirdfield in the data stream may be determined and, based on a determinationof the third field in the data stream, the process may send, to the datastore, a request for fourth information, associated with the user, in afourth field. The method may further comprise receiving, from the datastore, the fourth information, associated with the user, in the fourthfield; determining, based on the fourth field, a second TTL value forthe fourth information received from the data store; storing, in theapplication cache, the fourth information and the second TTL value;receiving, from the application, a request for the fourth information;and sending, to the application, the fourth information. In one or moreexamples, the third field may indicate a transaction associated with theuser has been declined, a change in behavior of the user, or a change inan account balance of an account associated with the user. The fourthfield may comprise a most recent transaction associated with an accountof the user.

In some aspects, the method may further comprise determining, at theserver and based on the fields in the data stream, updated secondinformation is being received; and determining, from the data stream,that the updated second information has been received in its entirety.The sending the second information to the application may be delayeduntil after a determination that the entirety of the updated secondinformation has been received by the application cache.

In some aspects, the method may further comprise receiving, at theapplication cache and for the user, a modification of the one or moreTTL values to be associated with the fields from the data stream. Thedetermining the one or more TTL values may comprise determining, basedon the modification of the one or more TTL values for the user, one ormore modified TTL values, for the information of the user, associatedwith the fields, and the storing the information and the one or more TTLvalues may comprise storing, in the application cache, the information,of the user, and the one or more modified TTL values.

An apparatus, in accordance with various aspects, may comprise one ormore processors; and memory storing instructions that, when executed bythe one or more processors, cause the apparatus to receive, from anapplication, user interaction information; predict, using amachine-learning model trained on previous interactions of previoususers with their applications and with call centers and based on thereceived user interaction information, a likelihood of a user of theapplication to contact a call center with an inquiry from the user; andpopulate a call center cache based on a prediction for the user tocontact a call center. The apparatus may further comprise instructionsthat cause the apparatus to authenticate the user based on theprediction for the user; store, in an application cache and with atime-to-live value, first information for the application; monitor adata stream of incoming information; detect second information in thedata stream; and store, based on the detecting the second information,the second information in the application cache, wherein the secondinformation overwrites the first information and refreshes thetime-to-live value.

In one or more further aspects, the instructions may cause the apparatusto receive interactions between the call center and the user; andretrain, based on the interactions between the call center and the userand based on the previous interactions of the previous users with theirapplications and with the call centers, the machine-learning model.

A non-transitory media storing instructions that, when executed by oneor more processors, cause the one or more processors to perform stepscomprising receiving, from the application, user interactioninformation; and predicting, using a machine-learning model trained onprevious interactions of previous users with their applications and withcall centers and based on the received user interaction information,whether the user is expected to contact a call center; populating a callcenter cache based on a prediction for the user. The method may furthercomprise receiving an inquiry from the user; determining, based on theprediction from the machine-learning model and from the inquiry by theuser, an authentication level to be used when authenticating the user;based on a determination of the authentication level, authenticating theuser to the call center using the determined authentication level;receiving interactions between the call center and the user; andretraining, based on the interactions between the call center and theuser and based on the previous interactions of the previous users withtheir applications and with the call centers, the machine-learningmodel.

Processes for Prepopulating a Cache Based on Previous AccountInteractions

FIG. 11 depicts a block diagram showing various process flows for usinginteractions associated with a first cache to prepopulate a secondcache. FIG. 11 pertains generally to prepopulating one cache based onevents that populated another cache. Steps 501-506 and 508 of FIG. 9correspond to steps 501-506 and 508, respectively, of FIG. 11 . In step1100, the application cache retrieves any needed information from thedata store. For example, the retrieval may comprise any of theapproaches described herein including, but not limited to the approachesof FIGS. 5-7 . In step 1101, information from the application cache isprepopulated into a first action fraud determination cache. In step1102, the system receives a first action request from a user, where thefirst action request of step 1102 corresponds to the first action frauddetermination cache that was prepopulated in step 1101. In step 1103,the system reviews the first action request (from step 1102) using theprepopulated first action fraud cache (from step 1101). In step 1104,the first action is approved or denied. If denied, the denial isprovided to the user in step 1105. If approved, the system prepopulatesa second action fraud determination cache is step 1106. Also, ifapproved, the approval is provided to the user in step 1107. In step1108, the system receives a request for a second action from the user.Based on the request for the second action from step 1108 and theprepopulated second action fraud determination cache from step 1106, thesystem reviews the second action request in step 1109.

A use case associated with FIG. 11 includes addressing actions that,while related to each other, may be handled separately—e.g., checkingfor fraud in opening a new account may be independent from checking forfraud in conducting a transaction using an existing account. Forexample, when a fraudster acquires an authorized user's phone runningthe application, the fraudster may attempt to open multiple accountsbased on the user's existing account. Opening a first new account mayresult in the population of a new account fraud determination cache. Theopening of the second and additional accounts may access the content inthe new account fraud determination cache, thus reducing the time forwhether any second or subsequent new account requests are from theauthorized user. If the fraudster is able to open one new account (withthe subsequent requests being denied), the fraudster may attempt toinitiate large transactions using that newly opened account. The processof FIG. 11 attempts to forestall the fraudster's new transaction byusing the recently populated new account fraud determination cache toprepopulate a new transaction fraud determination cache. In other words,FIG. 11 uses the prepopulation of the first action cache (in step 1101)to prepopulate a second action cache (in step 1106) even though bothcaches may be accessed independently of each other.

In accordance with the examples described herein, a computer-implementedprocess may include receiving, by a server and from an application,first information from an application cache associated with theapplication. The process may include prepopulating a first action cachewith at least some of the first information received from theapplication cache, receiving a first request, by the server and from theapplication, a first request comprising a request for performing a firstaction, and performing the first action. The process may includeprepopulating, with second information and based on the performance ofthe first action, a second action cache with at least some of the firstinformation from the first action cache, receiving a second request, bythe server and from the application, a second request comprising arequest for performing a second action, determining, by the server andbased on the second information in the prepopulated second cache,whether to perform the second action, and based on a determination toperform the second action, performing the second action.

In additional examples, the method may additionally include storing thefirst information in the first action cache, wherein the first actionassigns a time-to-live value to the first information and deleting, atan expiration of the time-to-live value of the first information, thefirst information from the first action cache. The method may includedetermining, based on receiving the first request from the application,whether to perform the first action, wherein performing the first actionis based on a determination to perform the first action. The method mayinclude receiving, by the server and from the application, a thirdrequest comprising a request for performing the first action,determining, by the server and based on the first information in theprepopulated first action cache, whether to perform the third request'sfirst action, and based on a determination to not perform the thirdrequest's first action, denying the third request.

The first action may include opening a new account, and the first actioncache may include a cache of information from which a determination ofwhether the first request for the first action is fraudulent. The secondaction may include performing a new transaction, and the second actioncache may include a cache of information from which a determination ofwhether the second request for the second action is fraudulent.

The method may further include monitoring a data stream of incominginformation, detecting user-specific information in the data streamassociated with a user associated with the application, wherein theuser-specific information may include a change to applicationinformation in an application cache associated with the application,storing, based on the detecting the user-specific information, theuser-specific information in the application cache, wherein theuser-specific information overwrites existing user-specific informationand refreshes a time-to-live value associated with the user-specificinformation in the application cache, receiving, from the application, arequest for the user-specific information associated with the user, andsending, to the application, the user-specific information.

The method may further include detecting second user-specificinformation in the data stream associated with the user, wherein thesecond user-specific information may include information not currentlystored in the application cache, and storing, based on the detecting thesecond user-specific information, the second user-specific informationin the application cache, wherein the second user-specific informationreceives a second time-to-live value, wherein the receiving the requestfor the user-specific information may include receiving a request forthe user-specific information and the second user-specific information,and wherein sending the user-specific information may include sending,to the application, the user-specific information and the seconduser-specific information.

The method may include receiving, from the application, a request forthird user-specific information associated with the user, determiningthe application cache does not currently store the third user-specificinformation, receiving, from the server, the third user-specificinformation and a third time-to-live value, storing, in the applicationcache, the third user-specific information and the third time-to-livevalue, and sending, to the application, the third user-specificinformation.

Some implementations described herein relate to an apparatus comprisingone or more processors and memory storing instructions that whenexecuted by the one or more processors cause the apparatus to populatean application cache with user-specific information from a data stream.The apparatus may be configured to receive, by a server and from anapplication, first information from an application cache associated withthe application, wherein the first information may include at least someof the user-specific information. The apparatus may be configured toprepopulate a first action cache with at least some of the firstinformation received from the application cache. The apparatus may beconfigured to receive a first request, by the server and from theapplication, a first request comprising a request for performing a firstaction. The apparatus may be configured to perform the first action. Theapparatus may be configured to prepopulate, with second information andbased on the performance of the first action, a second action cache withat least some of the first information from the first action cache. Theapparatus may be configured to receive a second request, by the serverand from the application, a second request comprising a request forperforming a second action. The apparatus may be configured todetermine, by the server and based on the second information in theprepopulated second cache, whether to perform the second action. Theapparatus may be configured to, based on a determination to perform thesecond action, perform the second action.

In some examples, the apparatus may further include instructions tofurther cause the apparatus to store the first information in the firstaction cache, wherein the first action assigns a time-to-live value tothe first information and delete, at an expiration of the time-to-livevalue of the first information, the first information from the firstaction cache.

In some examples, the apparatus may further include instructions tofurther cause the apparatus to determine, based on receiving the firstrequest from the application, whether to perform the first action,wherein performance of the first action is based on a determination toperform the first action.

In some examples, the apparatus may further include instructions tofurther cause the apparatus to receive, by the server and from theapplication, a third request comprising a request for performing thefirst action, determine, by the server and based on the firstinformation in the prepopulated first action cache, whether to performthe third request's first action, and based on a determination to notperform the third request's first action, denying the third request.

In some examples, the first action may include opening a new account.The first action cache may include a cache of information from which adetermination of whether the first request for the first action isfraudulent. The second action may include performing a new transaction.The second action cache may include a cache of information from which adetermination of whether the second request for the second action isfraudulent.

In some examples, the apparatus may further include instructions tofurther cause the apparatus to monitor the data stream for incominginformation, detect new user-specific information in the data streamassociated with a user associated with the application, wherein the newuser-specific information may include a change to user-specificinformation in the application cache associated with the application,store, based on the detecting the user-specific information, theuser-specific information in the application cache, wherein theuser-specific information overwrites existing user-specific informationand refreshes a time-to-live value associated with the user-specificinformation in the application cache, receive, from the application, arequest for the user-specific information associated with the user, andsend, to the application, the user-specific information.

In some examples, the apparatus may further include instructions tofurther cause the apparatus to detect second user-specific informationin the data stream associated with the user, wherein the seconduser-specific information may include information not currently storedin the application cache, and store, based on the detecting the seconduser-specific information, the second user-specific information in theapplication cache, wherein the second user-specific information receivesa second time-to-live value. The receiving the request for theuser-specific information may include receiving a request for theuser-specific information and the second user-specific information. Thesending the user-specific information may include sending, to theapplication, the user-specific information and the second user-specificinformation.

In some examples, the apparatus may further include instructions tofurther cause the apparatus to receive, from the application, a requestfor third user-specific information associated with the user, determinethe application cache does not currently store the third user-specificinformation, receive, from the server, the third user-specificinformation and a third time-to-live value, store, in the applicationcache, the third user-specific information and the third time-to-livevalue, and send, to the application, the third user-specificinformation.

The time-to-live value, the second time-to-live value, and thetime-to-live value may be different from each other.

One more non-transitory media may store instructions that, when executedby one or more processors, cause the one or more processors to performsteps comprising receiving, by a server and from an application, firstinformation from an application cache associated with the application,prepopulating a first action cache with at least some of the firstinformation received from the application cache, receiving a firstrequest, by the server and from the application, a first requestcomprising a request for performing a first action, determining, basedon receiving the first request from the application, whether to performthe first action, based on a determination to perform the first action,performing the first action, prepopulating, with second information andbased on the performance of the first action, a second action cache withat least some of the first information from the first action cache,receiving a second request, by the server and from the application, asecond request comprising a request for performing a second action,determining, by the server and based on the second information in theprepopulated second cache, whether to perform the second action, andbased on a determination to perform the second action, performing thesecond action.

Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A computer-implemented method comprising:receiving, at a server and from an application associated with a userdevice of a user, user interaction information; predicting, using amachine-learning model trained on previous interactions of other userswith their applications on their user devices and with call centers andbased on the received user interaction information, call centerinformation expected to be needed by a call center when responding to aninquiry, associated with the received user interaction information, fromthe user; populating a call center cache based on a prediction that theuser will contact the call center; receiving a result of a userinteraction between the user and the call center; storing, in anapplication cache and with a time-to-live value, first information forthe application; monitoring a data stream of incoming information;detecting second information in the data stream; storing, based on thedetecting the second information, the second information in theapplication cache, wherein the second information overwrites the firstinformation and refreshes the time-to-live value; retraining, based onthe result of the user interaction between the user and the call center,the machine-learning model, wherein the retraining of themachine-learning model is based on: the previous interactions of theother users with their applications on their user devices and with thecall centers; the received user interaction information; and the resultof the user interaction between the user and the call center; andrepopulating, based on the retraining of the machine-learning model,based on an updated prediction that the user will contact the callcenter, and based on the detection of the second information in the datastream the call center cache.
 2. The computer-implemented method ofclaim 1, further comprising: detecting third information in the datastream associated with the user, wherein the third information comprisesinformation not currently stored in the application cache; storing,based on the detecting the third information, the third information inthe application cache, wherein the third information receives a secondtime-to-live value; and receiving, from the application, a request forthe second information and the third information; and, sending, based onthe request for the second and the third information, the secondinformation and the third information.
 3. The computer-implementedmethod of claim 1, further comprising: receiving, from the application,a request for third information associated with the user; determiningthe application cache does not currently store the third information;receiving, from the server, the third information and a secondtime-to-live value; storing, in the application cache, the thirdinformation and the second time-to-live value; and sending, to theapplication, the third information.
 4. A computer-implemented methodcomprising: receiving, at a server and from an application associatedwith a user device of a user, user interaction information; predicting,using a machine-learning model trained on previous interactions of otherusers with their applications on their user devices and with callcenters and based on the received user interaction information, callcenter information expected to be needed by a call center whenresponding to an inquiry, associated with the received user interactioninformation, from the user; populating a call center cache based on aprediction that the user will contact the call center; receiving aresult of a user interaction between the user and the call center;receiving, at the server and from the application, a request for firstinformation associated with the user; storing the first information inan application cache, wherein the application cache assigns a firsttime-to-live (TTL) value to the first information; monitoring a datastream of incoming information; detecting second information in the datastream; storing, based on the detecting the second information, thesecond information in the application cache, wherein the secondinformation overwrites the first information and refreshes the first TTLvalue; retraining, based on the result of the user interaction betweenthe user and the call center, the machine-learning model, wherein theretraining of the machine-learning model is based on: the previousinteractions of the other users with their applications on their userdevices and with the call centers; the received user interactioninformation; and the result of the user interaction between the user andthe call center; and repopulating, based on the retraining of themachine-learning model, based on an updated prediction that the userwill contact the call center, and based on the detection of the secondinformation in the data stream, the call center cache.
 5. Thecomputer-implemented method of claim 4, further comprising: determining,at the application cache, that the first information, of the user, isnot currently stored in the application cache; sending, to a data storeand based on a determination that the first information of the user isnot currently stored in the application cache, a request for the firstinformation; receiving, from the data store, the first information;determining, based on the received first information, a second TTL valuefor the first information received from the data store; storing, in theapplication cache, the first information and the second TTL value; andsending the first information to the application.
 6. Thecomputer-implemented method of claim 5, wherein the first TTL value andthe second TTL value are the same.
 7. The computer-implemented method ofclaim 5, wherein the first TTL value and the second TTL value aredifferent.
 8. The computer-implemented method of claim 4, furthercomprising: determining, at the server and from the data stream, thirdinformation; storing, in the application cache, the third informationand a third TTL value; determining, at the server and from the datastream, fourth information; updating, based on the fourth information,the third information; and storing an updated third TTL value associatedthe third information.
 9. The computer-implemented method of claim 8,wherein the third information is associated with a current accountbalance of the user, and wherein the fourth information comprises achange in the current account balance, and wherein updating, based onthe fourth information, the current account balance results in anupdated account balance.
 10. The computer-implemented method of claim 4,further comprising: determining, from a table and for the firstinformation, the first TTL value associated with the first information.11. The computer-implemented method of claim 4, further comprising:determining, at the server, a third information in the data stream;based on a determination of the third information in the data stream,sending, to a data store, a request for fourth information, associatedwith the user; receiving, from the data store, the fourth information,associated with the user; determining, based on the fourth information,a second TTL value; storing, in the application cache, the fourthinformation and the second TTL value; receiving, from the application, arequest for the fourth information; and sending, to the application, thefourth information.
 12. The computer-implemented method of claim 11,wherein the fourth information indicates: a transaction associated withthe user has been declined, a change in behavior of the user, or achange in an account balance of an account associated with the user. 13.The computer-implemented method of claim 11, wherein the fourthinformation comprises a most recent transaction associated with anaccount of the user.
 14. The computer-implemented method of claim 4,further comprising: determining, at the server and based on informationin the data stream, updated second information is being received;receiving a request for the second information; determining, that theupdated second information has been received in its entirety; andsending, the updated second information to the application as the secondinformation, wherein the sending the second information to theapplication is delayed until after a determination that the entirety ofthe updated second information has been received by the applicationcache.
 15. The computer-implemented method of claim 4, furthercomprising: receiving, at the application cache and for the user, amodification of the TTL value to be associated with the firstinformation from the data stream, wherein the assignment, by theapplication cache of the TTL value, comprises determining, based on themodification of the TTL value for the user, a modified TTL value, forthe first information of the user, and wherein the storing the firstinformation and the TTL value comprises storing, in the applicationcache, the first information, of the user, and the modified TTL value.16. An apparatus comprising: one or more processors; and memory storinginstructions that, when executed by the one or more processors, causethe apparatus to: receive, at a server and from an application, userinteraction information associated with a user device of a user;predict, using a machine-learning model trained on previous interactionsof other users with their applications on their user devices and withcall centers and based on the received user interaction information,call center information expected to be needed by a call center whenresponding to an inquiry, associated with the received user interactioninformation, from the user; populate a call center cache based on aprediction that the user will contact the call center; receive a resultof a user interaction between the user and the call center; store, in anapplication cache and with a time-to-live value, first information forthe application; monitor a data stream of incoming information; detectsecond information in the data stream; store, based on the detection ofthe second information, the second information in the application cache,wherein the second information overwrites the first information andrefreshes the time-to-live value; retrain, based on the result of theuser interaction between the user and the call center, themachine-learning model, wherein the retraining of the machine-learningmodel is based on: the previous interactions of the other users withtheir applications on their user devices and with the call centers; thereceived user interaction information; and the result of the userinteraction between the user and the call center; and repopulate, basedon the retraining of the machine-learning model, based on an updatedprediction that the user will contact the call center, and based on thedetection of the second information in the data stream, the call centercache.
 17. One or more non-transitory media storing instructions that,when executed by one or more processors, cause the one or moreprocessors to perform steps comprising: receiving, at a server and froman application, user interaction information associated with a userdevice of a user; predicting, using a machine-learning model trained onprevious interactions of other users with their applications on theiruser devices and with call centers and based on the received userinteraction information, call center information expected to be neededby a call center when responding to an inquiry, associated with thereceived user interaction information, from the user; populating a callcenter cache based on a prediction that the user will contact the callcenter; receiving a result of a user interaction between the user andthe call center; storing, in an application cache and with atime-to-live value, first information for the application; monitoring adata stream of incoming information; detecting second information in thedata stream; storing, based on the detecting the second information, thesecond information in the application cache, wherein the secondinformation overwrites the first information and refreshes thetime-to-live value; retraining, based on the result of the userinteraction between the user and the call center, the machine-learningmodel, wherein the retraining of the machine-learning model is based on:the previous interactions of the other users with their applications ontheir user devices and with the call centers; the received userinteraction information; and the result of the user interaction betweenthe user and the call center; and repopulating, based on the retrainingof the machine-learning model, based on an updated prediction that theuser will contact the call center, and based on the detection of thesecond information in the data stream, the call center cache.